Knowledge-based spatial dose metrics and methods to generate beam orientations in radiotherapy

ABSTRACT

A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/121,235, field on Sep. 4, 2018, entitled“Knowledge-Based Spatial Dose Metrics and Methods to Generate BeamOrientations in Radiotherapy,” now U.S. Pat. No. 11,027,147, which is adivisional of and claims priority to U.S. patent application Ser. No.14/850,625, filed on Sep. 10, 2015, entitled “Knowledge-Based SpatialDose Metrics and Methods to Generate Beam Orientations in Radiotherapy,”now U.S. Pat. No. 10,080,911, the disclosures of which are herebyincorporated by reference in their entirety.

TECHNICAL FIELD

This description relates generally to the field of radiation therapy,and more particularly to radiation therapy treatment plan development.

BACKGROUND

Radiation therapy treatment plan development generally employs medicalimaging, such as X-ray, computed tomography (CT), magnetic resonanceimaging (MM), or the like. Typically, a series of two-dimensionalpatient images, each representing a two-dimensional cross-sectional“slice” of the patient anatomy, are used to reconstruct athree-dimensional representation of a volume of interest (VOI), orstructure of interest, from the patient anatomy.

The VOI typically includes one or more organs of interest, oftenincluding a planning target volume (PTV), such as a malignant growth oran organ including malignant tissue targeted for radiation therapy; arelatively healthy organ at risk (OAR) in the vicinity of a malignantgrowth at risk of radiation therapy exposure; or a larger portion of thepatient anatomy that includes a combination of one or more PTVs alongwith one or more OARs. The objective of the radiation therapy treatmentplan development typically aims to irradiate as much of the PTV as nearthe prescription dose as possible, while attempting to minimizeirradiation of nearby OARs.

The resulting radiation therapy treatment plans are used during medicalprocedures to selectively expose precise areas of the body, such asmalignant tumors, to specific doses of radiation in order to destroy theundesirable tissues. During the development of a patient-specificradiation therapy treatment plan, information generally is extractedfrom the three-dimensional model to determine parameters such as theshape, volume, location, and orientation of one or more PTVs along withone or more OARs.

Some existing radiation therapy planning tools have estimatedirradiation doses of OARs based on the simple linear, or Euclidean,distance between the PTV and each OAR. However, additional factorstypically have a significant impact on the effective radiation dosereceived by OARs in the general vicinity of the PTV. Existingmethodologies can have drawbacks when used to develop radiation therapyplans, since existing methods and tools do not accurately account forthese additional factors.

SUMMARY

According to one embodiment of the present invention, a system forestimating a dose from a radiation therapy plan includes a memory thatstores machine-readable instructions and a processor communicativelycoupled to the memory, the processor operable to execute theinstructions to subdivide a representation of a volume of interest intoa plurality of voxels. The processor also determines a plurality ofdistances, each of which is associated with a planned radiation fieldand a respective voxel. The processor further computes a plurality ofmetrics based on the plurality of distances, a plan parameter, and afield parameter. The processor also sums the plurality of metricscorresponding to the plurality of voxels. The summation of the pluralityof metrics represents an estimated dose received by the volume ofinterest from the planned radiation field.

According to another embodiment of the present invention, a method forestimating a dose from a radiation therapy plan includes subdividing arepresentation of a volume of interest into a plurality of voxels, anddetermining a distance associated with a planned radiation field and avoxel. The method further includes computing a metric based on thedistance, a plan parameter, and a field parameter.

According to yet another embodiment of the present invention, a methodfor estimating a dose from a radiation therapy plan includes generatingnormal vectors that emanate from points on a surface of a representationof a target volume and extend to a body surface. The method includesquantifying a dose fall-off curve along each of the normal vectors basedon the radiation therapy plan. The method also includes grouping asubset of the plurality of normal vectors based on a traversed organ atrisk, and determining a mean dose fall-off curve for the organ at riskbased on the subset. The method further includes deriving a dose-volumehistogram for the organ at risk based on the mean dose fall-off curveand a mean distance between the target volume and the organ at risk.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view illustrating an exemplary dose distributionplanning tool that employs spatial dose metrics to generate beamorientations in order to develop and evaluate patient-specific radiationtherapy treatment plans in accordance with an embodiment of the presentinvention.

FIG. 2 is a schematic view depicting an exemplary general computingsystem that can implement the dose distribution planning tool of FIG. 1.

FIG. 3 is an illustration of an exemplary radiation therapy patientanatomy that can be evaluated using the dose distribution planning toolof FIG. 1 .

FIG. 4 is an illustration of an exemplary normal vector and tangentialplane of a curved surface.

FIG. 5 is an illustration of an exemplary set of normal vectors on acomplex surface.

FIG. 6 is an illustration of an exemplary set of tangential lines from acurve.

FIG. 7 is an illustration of another exemplary radiation therapy patientanatomy that can be evaluated using the dose distribution planning toolof FIG. 1 .

FIG. 8 is an exemplary dose curve from within a planning target volumeto the body surface.

FIG. 9 is an illustration of exemplary dose gradient vectors from aplanning target volume in a radiation therapy patient anatomy.

FIG. 10 is an illustration of exemplary perpendiculars to dose gradientvectors.

FIG. 11 is an illustration of an exemplary projection of a dose gradientonto a sphere.

FIG. 12 is an illustration of exemplary meridians projected onto asphere from dose gradients.

FIG. 13 is an illustration of an exemplary thermal map representingprojected dose gradients.

FIG. 14 is an illustration of an exemplary custom bounding box.

FIG. 15 is an illustration of exemplary planes through isocenters.

FIG. 16 is flowchart depicting an exemplary process flow for employingspatial dose metrics to develop and evaluate patient-specific radiationtherapy treatment plans in accordance with an embodiment of the presentinvention.

FIG. 17 is a flowchart depicting another exemplary process flow foremploying spatial dose metrics to develop and evaluate patient-specificradiation therapy treatment plans in accordance with an embodiment ofthe present invention.

DETAILED DESCRIPTION

An embodiment of the present invention is shown in FIG. 1 , whichillustrates an exemplary dose distribution planning tool 10 that employsspatial dose metrics to generate beam orientations in order to developand evaluate patient-specific radiation therapy treatment plans. Thedose distribution planning tool 10 includes a patient geometryparametrization module 12, a beam orientation module 14, a doseprediction module 16, a knowledge assimilation module 18 and a knowledgebase 20. The dose distribution planning tool 10 correlates dosedistributions achieved in actual patients in radiotherapy treatmentswith the underlying patient anatomy, or geometry.

The dose distribution planning tool 10 predicts the three-dimensionaldose distribution for a new patient based on the patient anatomy andselects radiation beam orientations that have a relatively highprobability of achieving the predicted dose distribution, which is inpart a function of the beam orientation.

The patient geometry parametrization module 12 generates a set ofparameters, or metrics, based on the individual patient anatomicgeometry with respect to various factors that affect dose distribution.It is known in the art that the dose level outside of a target structuredecreases with linear, or Euclidean, distance from the target structure.However, additional geometric features can affect dose distribution.Metrics that take into account additional geometric features offerrelatively improved correlation between predicted dose distribution andstructure positions in patient geometry.

Examples of expected dose metrics in addition to OAR-target proximityinclude but are not limited to the volume of the target structure, thevolume of an organ at risk (OAR), any portion of the OAR that is notlocated within the field of the radiation beam, the number andorientation of applied fields, field geometry, target and OAR tissuedensities, the prescription dose, and the like. For example, variousmetrics can take into account the number of fields that converge at eachpoint in the patient geometry, or any organ passed through by a fieldbefore reaching the target volume. Additional metrics can account fortissue characteristics, for example, the Hounsfield unit (HU) scale canrepresent energy deposition and dispersion characteristics.

For example, the dose attenuation, or fall-off, profile is notsymmetric, but rather drops off much more rapidly as location extendsbeyond the vicinity of the target volume toward an out-of-field region.In addition, application of multiple fields with differing target doselevels at varying distances from a point in an OAR further complicatethe determination of an equivalent distance using combined metrics.

An embodiment of the present invention generates metrics with respect tothe target volume and OARs based on a geometrically-expectable dose(GED) distribution. GED metrics incorporate general assumptionsregarding how a clinical dose delivery is organized with respect to thetarget shape. GED metrics also take into account the geometry of thefield setup. The dose at any point in an OAR is equal to the sum of theindividual contributory doses at that location due to each of theapplied target-level fields.

In an embodiment, OAR geometry is included in GED metrics, for example,the number of voxels in OARs that are crossed by a field before reachingthe target volume. In another embodiment, conformal dose metrics areemployed, including descriptive setup and patient geometry factors. Thebeam orientation module 14 evaluates the patient geometry metrics anddetermines preferred beam geometry in the form of one or more beamorientations that meet the constraints for the target volume and OARs.

In an embodiment, the field intensity is modulated to tailor the dosedistribution to the specific target form. Target anatomical features,including, for example, the shape, elongation, and position of thetarget with respect to multiple fields, preferred directions, or beamorientations, are defined. Thus, for example, fields that areperpendicular to the target main direction are allowed to deliver lessradiation than fields that are parallel to the target main direction.

Further, in an embodiment, the intensity of radiation delivered across afield is modulated with respect to GED metrics assigned to each beamletwithin a field. For example, metrics can be defined regarding the numberof voxels the beamlet crosses, or the distance the beamlet travelsthrough the patient before reaching the target volume, and the intensityof the beamlet can be adjusted according to the metrics.

The dose prediction module 16 evaluates the dose distribution withrespect to a specific set of metrics and a specific grouping of beamorientations. The dose prediction module 16 employs a knowledge-baseddose prediction algorithm that predicts the viable dose distribution ona structure of interest based on a set of anatomical features. The doseprediction algorithm estimates the quality of treatment plan achievedbased on detailed planning for specific target geometries and fieldgeometries.

In an embodiment, the dose prediction module 16 permits interactivedefinition and fine-tuning of the target volume to be treated whileproviding an immediate estimate of the achievable plan quality. Thequality can be described, for example, by predicting the dose-volumehistograms (DVHs) that would be achieved for a specific target volume.The dose prediction module 16 can facilitate decisions, for example,regarding the clinical tradeoff between the size of spatial region to beirradiated and sparing of critical organs.

In another embodiment, the dose prediction module 16 permits interactivedefinition and modification of the radiation field geometry whileproviding an immediate estimate of the achievable plan quality. Thus,independent dose optimization would not be required for each candidatetarget volume and field geometry. Further, in an embodiment, the dosedistribution is evaluated with respect to multiple target volumes, forexample, adding weighted contributions, or fractionations, correspondingto the various modified distances from an OAR to multiple target volumeswith different dose levels.

In an embodiment, the geometrically expectable dose (GED) is calculated,whenever possible, using specific field geometry information, whichyields a relatively accurate and meaningful definition of the dosecontribution resulting from the out-of-field portion of the OAR. Whenthe field geometry is not available a priori, equally distributed fieldssurrounding the patient in the isocenter plane are considered. The GEDis the expected dose that a water-equivalent patient with the definedanatomical geometry would receive if the same amount of radiation weredelivered to the target from each field.

In various embodiments, the GED metrics are used in dose-volumehistogram (DVH) estimation, fluence estimation, or three-dimensionaldose estimation. In an embodiment, estimated dose distributions based onGED metrics are compared with corresponding dose distributions actuallyachieved in clinical treatments to tune an actual knowledge model.

The knowledge assimilation module 18 extracts major dosimetric featuresfrom existing datasets representing the actual historical patientpopulation. In knowledge-based dose prediction, information gleaned fromactual historical plans is used to estimate the achievable dosedistribution regarding a new patient. For example, patient geometry anddose information of multiple historical treatment plans are distilledinto a prediction model that can be used for dose prediction withoutstoring all of the information from the original set of plans.

The knowledge base 20 stores the existing datasets representing ahistorical population of actual patient anatomical and achieved doseinformation. The systems described herein can offer advantages such asevaluating plans with different field geometries, evaluating plans withmultiple target volumes with differing dose levels, and analyzing theeffect of target volume shape on dose distribution.

As illustrated in FIG. 2 , an exemplary general computing device 22 thatcan be employed in the dose distribution planning tool 10 of FIG. 1includes a processor 24, a memory 26, an input/output device (I/O) 28storage 30 and a network interface 32. The various components of thecomputing device 22 are coupled by a local data link 34, which invarious embodiments incorporates, for example, an address bus, a databus, a serial bus, a parallel bus, or any combination of these.

The computing device 22 communicates information to and requests inputfrom the user or other devices by way of the I/O 28, which in variousembodiments incorporates, for example, an interactive, menu-driven,visual display-based user interface, or graphical user interface (GUI),and a pointing device with which the user may interactively inputinformation using direct manipulation of the GUI.

The computing device 22 can be coupled to a communication network by wayof the network interface 32, which in various embodiments incorporates,for example, any combination of devices—as well as any associatedsoftware or firmware—configured to couple processor-based systems,including modems, access points, network interface cards, LAN or WANinterfaces, wireless or optical interfaces and the like, along with anyassociated transmission protocols, as may be desired or required by thedesign.

The computing device 22 can be used, for example, to implement thefunctions of the components of the dose distribution planning tool 10 ofFIG. 1 . In various embodiments, the computing device 22 can include,for example, a server, a controller, a workstation, a mainframecomputer, personal computer (PC), a note pad, a computing tablet, apersonal digital assistant (PDA), a smart phone, a wearable device, orthe like. Programming code, such as source code, object code orexecutable code, stored on a computer-readable medium, such as thestorage 30 or a peripheral storage component coupled to the computingdevice 22, can be loaded into the memory 26 and executed by theprocessor 24 in order to perform the functions of the dose distributionplanning tool 10.

Referring to FIG. 3 , an exemplary patient anatomy 50 is depicted withseveral structures of interest, including a primary target volume 52, asecondary target volume 54, and an organ at risk (OAR) 56. Three plannedradiation beams are also depicted, including a primary field 62, asecondary field 64 and a tertiary field 66. As used herein, the termsprimary, secondary and tertiary do not refer to relative priority ormagnitude, but rather, these terms are used to distinguish between thevarious volumes and fields.

As shown in FIG. 3 , the primary field 62 and the secondary field 64 aredirected toward the full scope of the primary target volume 52, and thetertiary field 66 is directed toward the scope of the secondary volume54, which is fully enveloped by the primary target volume 52. As aresult, the prescription dose planned to be delivered to the primarytarget volume 52, dose_(t1), includes the cumulative effect of both theprimary field 62 and the secondary field 64. The prescription doseplanned to be delivered to the secondary target volume 52, dose_(t2),includes the cumulative effect of each the primary, secondary and thetertiary fields 62, 64, and 66.

The patient geometry parametrization module 12 of FIG. 1 determines aset of parameters, or metrics, to represent the OAR 56 of FIG. 3 . In anembodiment, the OAR is subdivided into a number of individual volumepartitions, or voxels, that are individually evaluated with regard todose distribution. For example, three individual voxels 70 are depictedin FIG. 3 . Metrics are assigned to each voxel based on the distancefrom each field origin to the voxel, the prescription dose of anytarget(s) crossed by the corresponding field fanline, and a fieldparameter based on the nominal energy of the planned radiation field.Thus, the metrics take into account the field position and orientation.

For example, the left voxel 72 lies within the scope of the primaryfield 62 along a fanline 82 that passes through the primary targetvolume 52. Thus, the patient geometry parametrization module 12 assignsmetrics to the left voxel 72 that account for the strength of theprimary field 62 and the distance from the origin (not shown) of theprimary field 62 to the left voxel 72. For example, in an embodiment, astrength parameter, that depends on the nominal energy of the primaryfield 62 is assigned to the left voxel 72, along with a distanceparameter, D₁₁, that depends on the Euclidian distance from the primaryfield origin to the left voxel 72.

The right voxel 74 lies within the scope of both the primary field 62and the secondary field 64. Another fanline 84 of the primary field 62passing through the right voxel 74 passes through both the primarytarget volume 52 and the secondary target volume 54. A fanline 86 of thesecondary field 64 passing through the right voxel 74 also passesthrough the primary target volume 52. Thus, the patient geometryparametrization module 12 assigns metrics to the right voxel 74 thataccount for the strength of the primary field 62, the strength of thesecondary field 64, the distance from the primary field origin to theright voxel 74, the distance from the secondary field origin (not shown)to the right voxel 74, and the prescription doses.

For example, in an embodiment, the strength parameter, λ₁, that dependson the nominal energy of the primary field 62, as well as anotherstrength parameter, λ₂, that depends on the nominal energy of thesecondary field are assigned to the right voxel 74. In addition, adistance parameter, D₁₂, that depends on the Euclidian distance from theprimary field origin to the right voxel 74, as well as another distanceparameter, D₂₂, that depends on the Euclidian distance from the primaryfield origin to the right voxel 74 are assigned to the right voxel 74.

The dose prediction module 16 of FIG. 1 evaluates the planned dosedistribution delivered to the OAR by evaluating the expected dosecontribution received at each defined voxel in the OAR. In anembodiment, the geometrically-expected dose received at the left voxel72 of FIG. 3 is calculated using the metrics assigned to the left voxel72, for example, the strength parameter, λ₁, and the distance parameter,D₁₁, in the following formula:

$\begin{matrix}{{GED_{1}} = {{{dose}_{t1}\left( \frac{\left( D_{11} \right)e^{({{- \lambda_{1}}D_{11}})}}{\left( D_{11} \right)^{2}} \right)}.}} & (1)\end{matrix}$

Further, the geometrically-expected dose received at the right voxel 74is calculated using the metrics assigned to the right voxel 74, forexample, the strength parameters, λ₁ and λ₂, and the distanceparameters, D₁₂ and D₂₂. The geometrically-expected dose contribution atthe right voxel 74 due to the primary field 62 is calculated using thefollowing formula:

$\begin{matrix}{{GED}_{12} = {{{dose}_{t1}\left( \frac{\left( D_{12} \right)e^{({{- \lambda_{1}}D_{12}})}}{\left( D_{12} \right)^{2}} \right)} + {\left( {{dose}_{t2} - {dose}_{t1}} \right){\left( \frac{\left( D_{12} \right)e^{({{- \lambda_{1}}D_{12}})}}{\left( D_{12} \right)^{2}} \right).}}}} & \left( {2a} \right)\end{matrix}$ $\begin{matrix}{= {{{dose}_{t2}\left( \frac{\left( D_{12} \right)e^{({{- \lambda_{1}}D_{12}})}}{\left( D_{12} \right)^{2}} \right)}.}} & \left( {2b} \right)\end{matrix}$

The geometrically-expected dose contribution at the right voxel 74 dueto the secondary field 64 is calculated using the following formula:

$\begin{matrix}{{GED_{22}} = {{{dose}_{t1}\left( \frac{\left( D_{22} \right)e^{({{- \lambda_{2}}D_{22}})}}{\left( D_{22} \right)^{2}} \right)}.}} & (3)\end{matrix}$

As a result, the total geometrically-expected dose received at the rightvoxel 74 due to both the primary and secondary fields 62, 64 is providedby calculating the sum of the contributions of both fields 62, 64, forexample, summing the results of formulas (2) and (3) above in thefollowing formula:

$\begin{matrix}{{GED_{2}} = {{{dose}_{t2}\left( \frac{\left( D_{12} \right)e^{({{- \lambda_{1}}D_{12}})}}{\left( D_{12} \right)^{2}} \right)} + {{{dose}_{t1}\left( \frac{\left( D_{22} \right)e^{({{- \lambda_{2}}D_{22}})}}{\left( D_{22} \right)^{2}} \right)}.}}} & (4)\end{matrix}$

On the other hand, the out-of-field voxel 76 lies outside all threefields 62, 64, 66. Since no field fanlines cross the out-of-field voxel76, none of the three fields 62, 64, 66 has any contribution to the dosedistribution at the out-of-field voxel 76. Thus, thegeometrically-expected dose received at the out-of-field voxel 76 iszero (0). The dose prediction module 16 derives the achievabledose-volume histogram (DVH) regarding the OAR 36 from the summation ofthe contributions at all of the voxels of the OAR 36 from all of theplanned radiation fields.

FIG. 4 shows a normal vector, {circumflex over (n)}_(PTV) 172, and atangent plane, T_(⊥) 174, on the surface of a planning target volume176. FIG. 5 illustrates a set of normal vectors 178 from a complexplanning target volume surface 180. FIG. 6 illustrates tangential lines182 from a two-dimensional curve.

Referring now to FIG. 7 , another exemplary patient anatomy 90 isdepicted with several structures of interest, including a target volume92, a primary organ at risk (OAR) 94, a secondary OAR 96, and a tertiaryOAR 98. An embodiment of the present invention correlates achieved dosedistributions in actual historical patient radio therapy treatment plansto specific underlying patient anatomy and stores the resultantcorrelation information in the knowledge base 20 of FIG. 1 . Thecorrelation information in the knowledge base 20 is accessed in order topredict three-dimensional dose distribution for new patients based onpatient anatomy.

Normal vectors are computed originating at multiple points on thesurface of the target volume 92 to the body surface 100 of the patient.For example, in an embodiment, the patient geometry parametrizationmodule 12 of FIG. 1 computes normal vectors on a grid along thethree-dimensional surface of the target volume 92. As a simplifiedexample, normal vectors 102, or rays, are computed at various pointsalong the surface of a cross-section of the target volume 92. Eachnormal vector 102 is traced from the surface of the target volume 92 ina direction that is orthogonal to the localized area of the surface ofthe target volume 92 immediately surrounding the point from which thenormal vector 102 originates.

The dose prediction module 16 computes the dose along the normal vectors102 with respect to the distance from the surface of the target volume92. For example, in an embodiment, the dose is computed along the normalvectors 102 from within the target volume 92 to the body surface 100.Thus, the entire dose fall-off curve is determined along each normalvector 102 until exiting the body contour. For example, the historicalpatient population-derived average dose falloff curve is quantifiedalong the ray emanating along the normal vector 102.

The normal vectors 102 are grouped according to the organ(s) at riskintersected by each normal vector 102. As illustrated in FIG. 8 , themean dose, D(r) 184, along the portion of each of the normal vectors 102intersecting an OAR is recorded. For example, the mean dose iscalculated for the portion of normal vector 104 passing through the OAR98; the mean dose is calculated for the portion of the normal vector 122passing through the OAR 94; and the mean doses are calculated for theportions of the normal vectors 110, 112, and 114 passing through the OAR96. The prescription dose 188 and the PTV surface 190 are indicated inFIG. 8 .

In addition, the population variations, σ_(D)(r), in D(r) are trackedfor each OAR 94, 96, and 98. In an embodiment, the dose predictionmodule 16 uses the mean doses for each OAR 94, 96, and 98 to produce arapid estimation of achievable three-dimensional dose for the respectiveOAR 94, 96, and 98.

Rays are traced along the normals of the target surface, and thethree-dimensional dose distribution is estimated. The dose along thenormal ray between the PTV and OAR is a principal componentcharacterizing the three-dimensional dose distribution of a radiotherapyplan. Collapsing the three-dimensional data into quasi-directionaltwo-dimensional plots is as efficient, but more descriptive, thancollapsing into dose-volume histogram plots. Modeling is simple, and hasproven to be effective. Statistically-modeled results can be used torefine the results and performance of the model.

The dose prediction module 16 derives the achievable dose-volumehistogram (DVH) regarding the OARs 94, 96, 98 from the dose fall-offcurves associated with the respective normal vectors 104, 110, 112, 114,122 and the distances between the OARs 94, 96, 98 and the target volume92. The dose prediction module 16 computes the derivatives of the dosefall-off curves for each normal vector 102, as well as the dose gradientfor the mean dose,

${\overset{\rightarrow}{g} = \frac{\Delta D}{\Delta r}},$corresponding to each OAR 94, 96, and 98, and identifies areas requiringstrong dose gradients. In an alternative embodiment, thethree-dimensional data is collapsed into quasi-directionaltwo-dimensional plots, which retain more descriptive information thanDVH plots.

Referring to FIG. 9 , the radial dose gradients 224 are estimated alongeach ray. The dose gradient,

${\overset{\rightarrow}{g} = \frac{\Delta D}{\Delta r}},$is computed in each normal direction, where D=dose and r=radialdistance. Vectors are bigger in directions that have stronger gradients.The derivative of the dose fall-off curves

$\frac{\Delta D}{\Delta r}$can be computed.

$\frac{\Delta D}{\Delta r}$identifies zones where high gradients are needed to achieve the desiredor expected radiotherapy plan.

Referring to FIG. 10 , perpendiculars 214 to each gradient vectoridentify beam orientations, where ť=∥{right arrow over (g)}∥=the arcplane perpendicular to {right arrow over (g)}. The derivative of thedose fall-off curves

$\frac{\Delta D}{\Delta r}$are computed.

$\frac{\Delta D}{\Delta r}$identifies zones where high gradients are needed to achieve the desiredor expected radiotherapy plan.

The beam orientation module 14 back-projects the dose gradient strengthperpendicular to the planning target volume normal direction to identifypreferred beam orientations. The beam orientation module 14 determinesperpendicular vectors with respect to each gradient vector tangent tothe surface of the target volume. Beam orientations that areperpendicular to strong gradient directions can be emphasized toreinforce the gradient. Normals with gradient indicate arc planes ofpreference for beam orientation. Gradient strength can be projectedalong perpendiculars onto a 4 pi sphere into a heat map that predictsbeam orientation or trajectory.

For example, as shown in FIG. 11 , the beam orientation module 14projects the dose gradient 192,

${\overset{\rightarrow}{g} = \frac{\Delta D}{\Delta r}},$of the mean dose, D(r), for each normal vector 102, n, onto the plane194 perpendicular to the normal vector crossing the target volumeisocenter. The plane 194 (A×B) has a normal along the gradientdirection. The plane 194 forms a meridian 198 on a sphere 200 about theisocenter 202 of the target volume.

FIG. 12 shows gradients projected to meridians 204, 206, 208 around asphere isocenter 226. The beam orientation module 14 interpolatesbetween the meridians resulting from all of the normal vectors 102, andcreates a preference matrix, for example, the thermal plot or map 210illustrated in FIG. 13 . The resulting preference matrix indicatespreferred beam orientations to achieve high dose gradients.

Referring to FIG. 14 , a custom motion management bounding box 212 isconstructed around the target volume 92. In each normal direction, thebounding margin (Δr) for an acceptable dose error (Δd) is computed asfollows:

${\Delta r} = {\Delta{d/{\frac{\Delta D}{\Delta r}.}}}$

The beam orientation module 14 computes custom bounding shapes, orisoconfidence boundaries, around the target volume 92 reflecting thedose sensitivity to target position error. The dose gradient along therespective normal vector is used as the basis for dose-sensitivityanalysis as a function of distance to the target volume 92. The custombounding box can be constructed based on the distance margin at eachnormal vector location as indicated by the ratio of the maximumacceptable dose uncertainty to the gradient along the respective normalvector. The beam orientation module 14 identifies beam orientations thatare most sensitive to target motion in order to drive motion managementstrategy as a function of beam orientation.

Referring to FIG. 15 , three illustrations show planes 214 passingthrough isocenters 216 of planning target volumes. From each beamorientation, a two-dimensional projection 218 of the target 220intersected by a plane 214 perpendicular to the beam 222 is computed.

The knowledge assimilation module 18 records achieved dose profilesassociated with actual historical patient treatment plans, and trackspopulation variations in dose with respect to the distance from thesurface of the target volume 92. OARs intercepted by each surface normalray trace are determined, and a curve describing relative dose as afunction of distance from PTV surface along the normal direction isstored into a D(r) plot for each OAR intercepted by the ray. Forretrieval, the median achieved dose from the stored D(r) histogram toeach pixel along the ray is assigned according to its distance from thePTV.

For example, normal vectors 106, 108, 116, 118, 122, 124, 126 do nottraverse any OARs, so the resulting dose profiles are stored in the bodydose-distance plot in the knowledge base 20. Since normal vector 120traverses OAR 94, the resultant dose profile is stored in the OAR 94dose-distance plot and in the body dose-distance plot in the knowledgebase 20. Similarly, normal vector 104 traverses OAR 98, so the resultantdose profile is stored in the OAR 98 dose-distance plot and in the bodydose-distance plot in the knowledge base 20. Normal vectors 110, 112,114 traverse the OAR 96, so the resultant dose profiles are stored inthe OAR 96 dose-distance plot and in the body dose-distance plot in theknowledge base 20.

In an alternative embodiment, a Boolean step can be used to create abody-only category for additional refinement. In another alternativeembodiment, the surface of the target volume 92 is regularized using anormal vector smoothing methodology. In yet another alternativeembodiment, the ray is traced from the surface of the PTV to the bodycontour in the direction of the normal, and traced in the negativedirection from the PTV surface to the medial surface of the PTV.

Referring now to FIG. 16 , an exemplary process flow is illustrated thatmay be performed, for example, by the dose distribution planning tool 10of FIG. 1 to implement an embodiment of the method described in thisdisclosure for employing spatial dose metrics to generate beamorientations in order to develop and evaluate patient-specific radiationtherapy treatment plans. The process begins at block 130, where a volumeof interest, such as an organ at risk (OAR), is subdivided into a groupof voxels, for example, equal-sized, three-dimensional units, asdescribed above.

As further described above, in block 132, the distance from the plannedradiation beam field origin to each voxel is determined. Theprescription dose for each target is ascertained, in block 134, and thevalue of a parameter that depends on the nominal energy of each field isascertained, in block 136.

In block 138, as explained above, geometry-based expected dose (GED)metrics are computed for all voxels with respect to each plannedradiation field and each target traversed by a fanline passing throughthe respective voxels. The GED metrics may be optionally multiplied byan intrafield modulation factor, in block 140. The GED metrics may beoptionally multiplied by an interfield modulation factor, in block 142.Components shown with dashed lines in FIG. 16 are optional items thatmay not be included in all implementations.

In block 144, the GED metrics are summed for all voxels in the volume ofinterest with respect to each field. The summation of the GED metricsprovides an estimation of the total dose received by the volume ofinterest in the planned radiation therapy.

Referring now to FIG. 17 , another exemplary process flow is illustratedthat may be performed, for example, by the dose distribution planningtool 10 of FIG. 1 to implement an embodiment of the method described inthis disclosure for employing spatial dose metrics to generate beamorientations in order to develop and evaluate patient-specific radiationtherapy treatment plans. The process begins at block 150, where normalvectors are generated emanating from multiple points, for example,forming a grid, on the surface of a target volume, as described above.The normal vectors extend, for example, from the target volume surfaceto the body surface of the patient.

As further described above, in block 152, a dose fall-off curve isquantified along each of the normal vectors until exiting the body basedon the radiation therapy plan historical results of actual patienttherapy plans. The normal vectors are grouped into subsets that traverseeach organ at risk (OAR), in block 154. Based on the subset of normalvectors that pass through each OAR, as explained above, mean dose iscomputed for each OAR and for the population, in block 156, which can beused to provide a rapid estimation of the achievable three-dimensionaldose. In block 158, an achievable dose-volume histogram is derived forthe OARs based on the mean dose for each OAR and the mean distancebetween the target volume and each OAR, as further explained above.

In block 160, the dose gradient for the mean dose for each OAR iscomputed based on the dose falloff curve along each of the normalvectors, and areas needing strong gradients are identified. The dosegradients are projected along a plane that is orthogonal to each normalvector, in block 162. For example, in an embodiment, the dose gradientsare projected along tangential planes at the surface of the targetvolume.

In block 164 a preference matrix, such as a thermal map or plot, isgenerated based on the intersections of the projected dose gradients atthe surface of a sphere. Reasonable beam trajectories are derived fromthe thermal surface map. In block 166, preferred beam orientations aredetermined based on relatively high dose gradient areas of thepreference matrix.

Further, in block 168, an isoconfidence boundary is constructed aroundthe target volume based on the dose gradients to estimate confidencemargins. Custom bounding shapes are computed around the planning targetvolume without full dose calculation. Dose sensitivity to targetposition error can be determined. In block 170, the sensitivity of beamorientations with respect to target volume motion or uncertain anatomyis evaluated based on the isoconfidence boundary. The results drivemotion management strategy as a function of beam orientation.

Aspects of this disclosure are described herein with reference toflowchart illustrations or block diagrams, in which each block or anycombination of blocks can be implemented by computer programinstructions. The instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing system to effectuate a machine or articleof manufacture, and when executed by the processor the instructionscreate means for implementing the functions, acts or events specified ineach block or combination of blocks in the diagrams.

In this regard, each block in the flowchart or block diagrams maycorrespond to a module, segment, or portion of code that including oneor more executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functionality associated with any block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, orblocks may sometimes be executed in reverse order.

A person of ordinary skill in the art will appreciate that aspects ofthis disclosure may be embodied as a device, system, method or computerprogram product. Accordingly, aspects of this disclosure, generallyreferred to herein as circuits, modules, components or systems, may beembodied in hardware, in software (including firmware, residentsoftware, micro-code, etc.), or in any combination of software andhardware, including computer program products embodied in acomputer-readable medium having computer-readable program code embodiedthereon.

In this respect, any combination of one or more computer readable mediamay be utilized, including, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of these. In the context of thisdisclosure, a computer readable storage medium may include any tangiblemedium that is capable of containing or storing program instructions foruse by or in connection with a data processing system, apparatus, ordevice.

Computer program code for carrying out operations regarding aspects ofthis disclosure may be written in any combination of one or moreprogramming languages. The program code may execute entirely on anindividual personal computer, as a stand-alone software package, partlyon a client computer and partly on a remote server computer, entirely ona remote server or computer, or on a cluster of distributed computernodes.

It will be understood that various modifications may be made. Forexample, useful results still could be achieved if steps of thedisclosed techniques were performed in a different order, and/or ifcomponents in the disclosed systems were combined in a different mannerand/or replaced or supplemented by other components. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A method for estimating a dose from a radiationtherapy plan, the method comprising: generating a plurality of normalvectors that emanate from a plurality of points on a surface of arepresentation of a target volume and extend to a body surface;generating a dose fall-off curve along each of the normal vectors basedon the radiation therapy plan, to produce a plurality of dose fall-offcurves; selecting a subset of the plurality of normal vectors thattraverse an organ at risk; determining a mean dose fall-off curve forthe organ at risk based on the subset; determining a dose-volumehistogram for the organ at risk based on the mean dose fall-off curveand a mean distance between the target volume and the organ at risk; anddetermining preferred beam orientations based on the dose fall-off curvealong each of the normal vectors.
 2. The method of claim 1, wherein thegenerating the dose fall-off curve along each of the normal vectorscomprises accessing stored patient results associated with historicaltherapy plans.
 3. The method of claim 1, further comprising: determininga dose gradient corresponding to each of the normal vectors based on thedose fall-off curve along each of the normal vectors; projecting thedose gradient corresponding to each of the normal vectors along a planethat is orthogonal to the normal vector; and generating a preferencematrix on the surface of a sphere based on the projected dose gradients,the preference matrix including a thermal surface map based onintersections of the projected dose gradients; wherein the determiningthe preferred beam orientations is based on the preference matrix. 4.The method of claim 1, further comprising: determining a dose gradientcorresponding to each of the normal vectors based on the dose fall-offcurve along each of the normal vectors; defining a boundary around thetarget volume based on the dose gradients; and determining a sensitivityof a beam orientation with respect to a target position error.
 5. Themethod of claim 4, wherein the boundary is defined based on a distancemargin at each location of the normal vectors, wherein the distancemargin is indicated by a ratio of a maximum acceptable dose uncertaintyto the dose gradient along a respective normal vector of the normalvectors.
 6. The method of claim 1, further comprising determining athree-dimensional dose for the organ at risk using the mean dosefall-off curve.
 7. The method of claim 1, further comprising:determining first derivatives of the dose fall-off curves; determining asecond derivative of the mean dose fall-off curve; and identifying adose gradient that achieves the radiation therapy plan using the firstderivatives and the second derivative.
 8. A system for estimating a dosefrom a radiation therapy plan, the system comprising: a memory thatstores machine-readable instructions; and a processor communicativelycoupled to the memory, wherein the processor is operable to execute themachine-readable instructions to generate a plurality of normal vectorsthat emanate from a plurality of points on a surface of a representationof a target volume and extend to a body surface, quantify a dosefall-off curve along each of the normal vectors based on the radiationtherapy plan, to produce a plurality of dose fall-off curves, group asubset of the plurality of normal vectors that traverse an organ atrisk, determine a mean dose fall-off curve for the organ at risk basedon the subset, derive a dose-volume histogram for the organ at riskbased on the mean dose fall-off curve and a mean distance between thetarget volume and the organ at risk, and determine preferred beamorientations based on the dose fall-off curve along each of the normalvectors.
 9. The system of claim 8, wherein the processor is furtheroperable to execute the machine-readable instructions to access storedpatient results associated with historical therapy plans.
 10. The systemof claim 8, wherein the processor is further operable to execute themachine-readable instructions to: compute a dose gradient correspondingto each of the normal vectors based on the dose fall-off curve alongeach of the normal vectors; project the dose gradient corresponding toeach of the normal vectors along a plane that is orthogonal to thenormal vector; and generate a preference matrix on the surface of asphere based on the projected dose gradients, the preference matrixincluding a thermal surface map based on intersections of the projecteddose gradients; wherein the preferred beam orientations are determinedbased on the preference matrix.
 11. The system of claim 8, wherein theprocessor is further operable to execute the machine-readableinstructions to: compute a dose gradient corresponding to each of thenormal vectors based on the dose fall-off curve along each of the normalvectors; construct a boundary around the target volume based on the dosegradients; and evaluate a sensitivity of a beam orientation with respectto a target position error.
 12. The system of claim 11, wherein theprocessor is further operable to execute the machine-readableinstructions to: construct the boundary based on a distance margin ateach location of the normal vectors, and wherein the distance margin isindicated by a ratio of a maximum acceptable dose uncertainty to thedose gradient along a respective normal vector of the normal vectors.13. The system of claim 8, wherein the processor is further operable toexecute the machine-readable instructions to determine athree-dimensional dose for the organ at risk using the mean dosefall-off curve.
 14. The system of claim 8, wherein the processor isfurther operable to execute the machine-readable instructions to:compute first derivatives of the dose fall-off curves; compute a secondderivative of the mean dose fall-off curve; and use the firstderivatives and the second derivative to identify a dose gradient neededto achieve the radiation therapy plan.
 15. A non-transitorycomputer-readable medium having computer-readable program code embodiedthereon that, when executed by a processor, causes the processor toperform a method comprising: generating a plurality of normal vectorsthat emanate from a plurality of points on a surface of a representationof a target volume and extend to a body surface; generating a dosefall-off curve along each of the normal vectors based on a radiationtherapy plan, to produce a plurality of dose fall-off curves; selectinga subset of the plurality of normal vectors that traverse an organ atrisk; determining a mean dose fall-off curve for the organ at risk basedon the subset; determining a dose-volume histogram for the organ at riskbased on the mean dose fall-off curve and a mean distance between thetarget volume and the organ at risk; and determining preferred beamorientations based on the dose fall-off curve along each of the normalvectors.
 16. The non-transitory computer-readable medium of claim 15,wherein the generating the dose fall-off curve along each of the normalvectors comprises accessing stored patient results associated withhistorical therapy plans.
 17. The non-transitory computer-readablemedium of claim 15, wherein the method further comprises: determining adose gradient corresponding to each of the normal vectors based on thedose fall-off curve along each of the normal vectors; projecting thedose gradient corresponding to each of the normal vectors along a planethat is orthogonal to the normal vector; and generating a preferencematrix on the surface of a sphere based on the projected dose gradients,the preference matrix including a thermal surface map based onintersections of the projected dose gradients; wherein the determiningthe preferred beam orientations is based on the preference matrix. 18.The non-transitory computer-readable medium of claim 15, wherein themethod further comprises: determining a dose gradient corresponding toeach of the normal vectors based on the dose fall-off curve along eachof the normal vectors; defining a boundary around the target volumebased on the dose gradients; and determining a sensitivity of a beamorientation with respect to a target position error; wherein theboundary is defined based on a distance margin at each location of thenormal vectors, wherein the distance margin is indicated by a ratio of amaximum acceptable dose uncertainty to the dose gradient along arespective normal vector of the normal vectors.
 19. The non-transitorycomputer-readable medium of claim 15, wherein the method furthercomprises determining a three-dimensional dose for the organ at riskusing the mean dose fall-off curve.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the method furthercomprises: determining first derivatives of the dose fall-off curves;determining a second derivative of the mean dose fall-off curve; andidentifying a dose gradient that achieves the radiation therapy planusing the first derivatives and the second derivative.